Circulating Biomarkers of One-Carbon Metabolism in Relation to Renal Cell Carcinoma Incidence and Survival

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Citation

Johansson, M., A. Fanidi, D. C. Muller, J. K. Bassett, Ø. Midttun, S. E. Vollset, R. C. Travis, et al. 2014. “Circulating Biomarkers of One-Carbon Metabolism in Relation to Renal Cell Carcinoma Incidence and Survival.” JNCI Journal of the National Cancer Institute 106 (12): dju327. doi:10.1093/jnci/dju327. http://dx.doi.org/10.1093/jnci/dju327.

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doi:10.1093/jnci/dju327

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DOI:10.1093/jnci/dju327 First published online November 5, 2014

Article

© The Author 2014. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]

Circulating Biomarkers of One-Carbon Metabolism in Relation to Renal Cell Carcinoma Incidence and Survival Mattias Johansson*,†, Anouar Fanidi*, David C. Muller*, Julie K. Bassett*, Øivind Midttun, Stein Emil Vollset, Ruth C. Travis, Domenico Palli, Amalia Mattiello, Sabina Sieri, Antonia Trichopoulou, Pagona Lagiou, Dimitrios Trichopoulos, Börje Ljungberg, Göran Hallmans, Elisabete Weiderpass, Guri Skeie, Carlos A. González, Miren Dorronsoro, Petra H. Peeters, H. B(as). Buenode-Mesquita, Martine M. Ros, Marie-Christine Boutron Ruault, Guy Fagherazzi, Françoise Clavel, María-José Sánchez, Aurelio Barricarte Gurrea, Carmen Navarro, J. Ramon Quiros, Kim Overvad, Anne Tjønneland, Krassimira Aleksandrova, Paolo Vineis, Marc J. Gunter, Rudolf Kaaks, Graham Giles, Caroline Relton, Elio Riboli, Heiner Boeing, Per Magne Ueland†, Gianluca Severi†, Paul Brennan† * Authors contributed equally to this work. † Senior authors.

Manuscript received December 31, 2013; revised April 11, 2014; accepted September 3, 2014. Correspondence to: Mattias Johansson, PhD, Genetic Epidemiology Group, International Agency for Research on Cancer (IARC/WHO), 150 cours Albert Thomas, 69008 Lyon, France (e-mail: [email protected]).

Background

The etiology of renal cell carcinoma (RCC) is only partially understood, but a metabolic component appears likely. We investigated biomarkers of one-carbon metabolism and RCC onset and survival.



Methods

The European Prospective Investigation into Cancer and Nutrition (EPIC) recruited 385 747 participants with blood samples between 1992 and 2000, and this analysis included 556 RCC case-control pairs. A subsequent replication study included 144 case-control pairs nested within the Melbourne Collaborative Cohort Study (MCCS). Plasma concentrations of vitamin B2, vitamin B6, folate, vitamin B12, methionine and homocysteine were measured in prediagnostic samples and evaluated with respect to RCC risk using conditional and unconditional logistic regression models, and to all-cause mortality in RCC cases using Cox regression models. All statistical tests were two-sided.



Results

EPIC participants with higher plasma concentrations of vitamin B6 had lower risk of RCC, the odds ratio comparing the 4th and 1st quartiles (OR4vs1) being 0.40 95% confidence interval [CI] = 0.28 to 0.57, Ptrend < .001. We found similar results after adjusting for potential confounders (adjusted Ptrend < .001). In survival analysis, the hazard ratio for all-cause mortality in RCC cases when comparing the 4th and 1st quartiles (HR4vs1) of vitamin B6 was 0.57 (95% CI = 0.37 to 0.87, Ptrend < .001).



Subsequent replication of these associations within the MCCS yielded very similar results for both RCC risk (OR4vs1  =  0.47, 95% CI  =  0.23 to 0.99, Ptrend  =  .07) and all-cause mortality (HR4vs1  =  0.56, 95% CI  =  0.27 to 1.17, Ptrend = .02). No association was evident for the other measured biomarkers.

Conclusion

Study participants with higher circulating concentrations of vitamin B6 had lower risk of RCC and improved survival following diagnosis in two independent cohorts.



JNCI J Natl Cancer Inst (2014) 106(12): dju327 doi:10.1093/jnci/dju327

The etiology of kidney cancer is not well understood, and there are notable unexplained differences in incidence. The highest rates worldwide are observed in the Czech Republic (1), and in the United States; the age-standardized rates (ASR) of kidney cancer are approximately two-fold higher for African Americans and European Americans than for Asians (2). Renal cell carcinoma (RCC) is the predominant type of kidney cancer and accounts for approximately 80% of cases (2). jnci.oxfordjournals.org

Established risk factors for RCC include tobacco smoking, obesity, and hypertension, as well as recently discovered gene variants (2–4). Additionally, it has been suggested that diabetes mellitus may increase RCC risk, whereas lifestyle factors such as high physical activity, alcohol, and intake of fruits and vegetables may reduce risk (2). The relation between fruit and vegetable intake and RCC is intriguing and consistently observed in both retrospective and prospective case-control studies (5). While residual confounding JNCI | Article 1 of 11

by tobacco smoking is of concern in the interpretation of these studies, a causal association cannot be excluded (6–8). Fruits and vegetables are sources of B vitamins and other components of the one-carbon metabolism pathway, which is important in maintaining DNA methylation and DNA repair mechanisms in the body (9,10). Circulating concentrations of B-vitamins have been investigated in relation to multiple cancers, but only one prospective study has been published for RCC (11). We sought to investigate whether concentrations of circulating B-vitamins and amino acids in the one-carbon metabolism pathway are related to RCC incidence and outcome using a large European cohort. To ensure the validity of our findings, we also conducted a replication of promising results within a separate Australian cohort.

Methods Study Cohort—The European Prospective Investigation into Cancer and Nutrition (EPIC) The EPIC study recruitment procedures have been previously described in detail (12), and important cohort information and follow-up procedures are provided in the Supplementary Methods (available online). Selection of Cases and Controls We initially identified 905 cases within EPIC that were diagnosed with RCC as C64.9 according to the International Classification of Diseases for Oncology, Second Edition (ICD-O-2). After excluding prevalent cases and cases with a prior history of another cancer (n = 85, except nonmelanoma skin cancer), cases who did not donate a blood sample (n = 153), were not histologically confirmed (n = 27), did not have questionnaire information available (n = 6), and cases from the Malmö center that did not participate in this study (n = 64), 570 RCC cases remained eligible. For each case, one control was randomly chosen from risk sets consisting of all cohort members alive and free of cancer (except nonmelanoma skin cancer) at the time of diagnosis of the index case. Matching criteria were: country, sex, date of blood collection (± one month, relaxed to ± five months for sets without available controls), and date of birth (± one year, relaxed to ± five years). Additionally, we included 553 controls (control group 2) matched to cases of a parallel study of head and neck cancer using identical matching criteria. Biochemical analyses were undertaken in the same laboratory under the same conditions, and at the same time for all cases and controls. After excluding sets with only one case or control, 556 casecontrol sets remained, as well as 553 additional unmatched controls from control group 2 that contributed to unconditional and stratified analyses. Replication Study—The Melbourne Collaborative Cohort Study (MCCS) To replicate promising associations, we designed a case-control study nested within the MCCS. Extensive details on recruitment and follow-up have been published previously (also see Supplementary Methods, available online) (13,14). Incident cases and controls were selected using the same protocol as the EPIC

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study. A total of 144 case-control pairs were available. The MCCS data were used as an independent replication analysis and were not pooled with the EPIC data. Biochemical Analyses Plasma samples were sent on dry ice to the Bevital A/S laboratory (http://www.bevital.no) in Bergen, Norway, where vitamin B2 (riboflavin), vitamin B6 (measured as pyridoxal 5’-phosphate, its active form), folate (vitamin B9), vitamin B12 (cobalamin), total homocysteine, and methionine were measured. Cotinine was measured as an indicator of recent smoking behavior. Details of the biochemical analyses are provided in Supplementary Methods (available online) (15–19). Statistical Analyses Quartiles of plasma concentrations for each biomarker were calculated based on the distribution among controls. Odds ratios (ORs) and 95% confidence intervals (CIs) of RCC were calculated relative to the first quartile using conditional logistic regression, conditioning on individual case set. Log-linear trends (Ptrend) were calculated by including the base 2 logarithm (log2) of the biomarker concentration as a continuous variable in separate models. To assess the consistency of any association, we compared RCC cases with the additional unmatched controls (control group 2) using unconditional logistic regression, adjusting for sex, country, and age at recruitment (five-year groups). We also compared the RCC cases with all EPIC controls in order to increase the statistical power. To evaluate whether known risk factors of RCC could explain any association, we assessed if OR estimates were affected after including indicator variables in the logistic regression models for tobacco smoking (smoking status at baseline [never, former, current] and quartiles of cotinine concentrations [determined by the distribution for current smokers]), alcohol intake at recruitment (g/day), lifetime alcohol intake [ever/never], obesity (indicated by body mass index [BMI], five categories: .06). In contrast, participants with higher concentrations of vitamin B6 had a lower risk of RCC in a dose response fashion (Ptrend < .001), the OR when comparing the 4th and 1st quartiles (OR4vs1) being 0.40 (95% CI = 0.28 to 0.57). Adjusting for potential confounders did not notably affect the OR estimates (adjusted OR4vs1 = 0.43, 95% CI = 0.29 to 0.64, Ptrend < .001) (Table 2). In a sensitivity analysis in which participants with missing covariate information were excluded, the corresponding adjusted OR4vs1 was 0.34 (95% CI = 0.21 to 0.57, Ptrend < .001) (Supplementary Table 2, available online). After accounting for vitamin B6, the other plasma biomarkers (vitamin B2, folate, B12, homocysteine and methionine) did not display any association with RCC risk (data not shown). When comparing RCC cases with control group 2, we observed similar associations (Supplementary Table  2, available online).

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After combining all EPIC controls in an unconditional analysis the unadjusted OR4vs1 was 0.43 (95% CI = 0.32 to 0.60, Ptrend < .001) and 0.49 (95% CI = 0.35 to 0.68, Ptrend < .001) after adjusting for risk factors (Supplementary Table 2, available online). Replication Study In order to determine if the inverse relation between vitamin B6 and risk of RCC was restricted to the EPIC study population, we analyzed plasma concentrations of vitamin B6 and cotinine in 144 additional case-control pairs nested within the MCCS. The distribution of vitamin B6 within the MCCS was similar to that within EPIC; hence, the same quartile cutoff points were applied. We obtained very similar OR estimates of RCC for quartiles of vitamin B6 within the MCCS, the OR4vs1 being 0.47 (95% CI  =  0.23 to 0.99), after adjusting for available risk factors (Table 3). Associations With a Doubling of Plasma Vitamin B6 Levels A doubling in plasma vitamin B6 was associated with a 20% lower odds of RCC in MCCS (OR for log2B6 [ORlog2]  =  0.80, 95% CI  =  0.64 to 1.02, Ptrend  =  .07) and a 22% lower odds in EPIC (ORlog2 = 0.78, 95% CI = 0.63 to 0.82, Ptrend < .001). The corresponding unadjusted ORlog2 estimates after stratifying by various descriptive variables within EPIC are displayed in Figure 1. The association between concentrations of vitamin B6 and RCC risk was marginally more prominent for men than women, for current smokers than former and never smokers, and for subjects that were not hypertensive. The ORlog2 for vitamin B6 among current smokers, after adjusting for hypertension, waist-to-hip ratio, educational attainment, alcohol intake, BMI, and circulating cotinine was 0.52 (95% CI = 0.39 to 0.71). Additional adjustment for number of cigarettes smoked per day and duration of smoking did not affect the estimate (ORlog2 = 0.53, 95% CI = 0.39 to 0.72). We further note that the association between vitamin B6 and risk was evident when evaluating blood samples taken up to ten years prior to diagnosis (Figure 1). Circulating concentrations of vitamin B6 by demographic variables, risk factors, and tumor stage are provided in Supplementary Table  3 (available online). All-Cause Mortality for RCC Cases Results of Cox proportional hazards regression for all-cause mortality (205 deaths in total) are shown in Table  4 and Supplementary Table  4 (available online). For vitamin B6 the HR for all-cause mortality for RCC cases when comparing the 4th and 1st quartiles was 0.57 (95% CI = 0.37 to 0.87) (Table 4). The corresponding Ptrend was less than .001, and the trend HR for log2B6 (HRlog2) was 0.74 (95% CI = 0.62 to 0.88). This result was nearly identical when excluding cases diagnosed within two years of blood draw (HRlog2 = 0.73, 95% CI = 0.60 to 0.90, Ptrend  =  .003). For RCC cause-specific mortality (147 deaths), the HR4vs1 was 0.36 (95% CI = 0.20 to 0.65), the corresponding HRlog2 was 0.64 (95% CI = 0.52 to 0.79, Ptrend